A/B testing specialists for your dedicated team.

Where A/B testing fits in a long-term product team.

The statistics are less forgiving than the tooling suggests. Sample size must be calculated before the test starts, from the smallest effect that matters; stopping when the result looks significant, known as peeking, inflates the false positive rate substantially, because a random walk crosses a threshold eventually. A test stopped early on a promising number is closer to a coin flip than to evidence.

Most product changes produce no detectable effect, and teams are consistently unprepared for that. A realistic programme expects a majority of tests to be inconclusive, which is itself valuable information: it says the change did not matter enough to build on.

What your dedicated team does with A/B testing.

Experimentation is a capability, and the tool is the smallest part of it. It needs a truly random assignment process, metrics defined before the test, a decision rule agreed in advance, and someone who will hold the line when a stakeholder wants to stop early.

Building that discipline takes time on the same product, which is what a dedicated development team provides.

Common A/B testing use cases.

• Changes evaluated on evidence A measured causal effect, free of the confounds in a before-and-after comparison.
• Sample size fixed before the start A stopping rule set in advance, so the result is not manufactured by peeking.
• Inconclusive treated as an answer Accepting that most changes do not move the metric, and acting on that.

Adding A/B testing skills to your team.

Experimentation sits with a dedicated data engineering team when the measurement pipeline is the constraint, and alongside product engineering when the assignment mechanism is.

You can add A/B testing capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.